Supercooling preservation technology, as a groundbreaking innovation in the field of organ preservation, significantly reduces the metabolic rate of cells and inhibits ice crystal formation by placing organs in a low-temperature environment near or below the freezing point. This technology extends the preservation time of organs and maintains their biological activity. Compared with the traditional low-temperature preservation at 4 °C, supercooling preservation effectively avoids cell damage and the accumulation of metabolic products, demonstrating significant advantages in the preservation of cells, tissues and organs. In recent years, important progress has been made in the optimization of cryoprotectants, the application of antifreeze proteins, the improvement of vitrification technology, and the development of nanotechnology-based rewarming techniques. These advancements provide new pathways to address the challenges of toxicity, ice crystal formation and uneven rewarming rates during supercooling preservation. This review summarizes the basic principles of supercooling preservation, the application of key technologies, and their practical effects in organ transplantation. It also analyzes the challenges of toxicity and rewarming efficiency, aiming to provide theoretical support and research directions for the future optimization of organ low-temperature preservation technology and its clinical application.

The mechanisms underlying autophagic defects in nonalcoholic steatohepatitis (NASH) remain largely unknown. We aimed to elucidate the roles of hepatic cyclooxygenase 1 (COX1) in autophagy and the pathogenesis of diet-induced steatohepatitis in mice. Human nonalcoholic fatty liver disease (NAFLD) liver samples were used to examine the protein expression of COX1 and the level of autophagy. Cox1^Δhepa mice and their wildtype littermates were generated and fed with 3 different NASH models. We found that hepatic COX1 expression was increased in patients with NASH and diet-induced NASH mice models accompanied by impaired autophagy. COX1 was required for basal autophagy in hepatocytes and liver specific COX1 deletion exacerbated steatohepatitis by inhibiting autophagy. Mechanistically, COX1 directly interacted with WD repeat domain, phosphoinositide interacting 2 (WIPI2), which was crucial for autophagosome maturation. Adeno-associated virus (AAV)-mediated rescue of WIPI2 reversed the impaired autophagic flux and improved NASH phenotypes in Cox1^Δhepa mice, indicating that COX1 deletion-mediated steatohepatitis was partially dependent on WIPI2-mediated autophagy. In conclusion, we demonstrated a novel role of COX1 in hepatic autophagy that protected against NASH by interacting with WIPI2. Targeting the COX1-WIPI2 axis may be a novel therapeutic strategy for NASH.

Male infertility has evolved from a common reproductive system disease to a major social issue. Youjing granule (YG) is a Chinese medicinal material used as a therapy method for tonifying the kidneys and removing dampness due to its pathogenic characteristics. YG has been shown to regulate sperm quality in clinical trials, but the underlying mechanism is not fully understood. The present study was aimed to explore the protective effects and mechanism of action of YG on male reproductive system damage caused by methyl methane sulfonate (MMS). We first established an infertility model of rats through oral administration of MMS and then treated with YG. To determine the effect of YG, spermatogenesis, microvascular density, and secretory function of Leydig cells and Sertoli cells in rats were assessed. Spermatogonial stem cells (SSCs) were co-cultured with mouse embryo fibroblast (MEF) cells as an in vitro cell model before exposure to serum containing YG. Furthermore, the proliferation and apoptosis of SSCs were measured. Results indicated that YG increased the expression of self-renewal and proliferation-related molecules such as glial cell line derived neurotrophic factor (GDNF) and fibroblast growth factor-2 (FGF2), and improved the quality of sperm and the proliferation of SSCs. In conclusion, YG may protect spermatogenetic function of rats through regulating the proliferation and self-renewal of SSCs.
Animals ; Cell Proliferation ; Male ; Mice ; Rats ; Semen ; Spermatogenesis ; Spermatogonia ; Stem Cells

AIM: To investigate the relationship between human cholesteryl ester transfer protein CETP gene polymorphism and postoperative neurocognitive disorders (PND). METHODS: A total of 124 elderly patients over 65 years of age who underwent elective non-cardiac surgery were enrolled in the study while 25 healthy volunteers matching age and sex were recruited as the control group. Neuropsychological tests were performed 1 day before surgery, 7 days, and 3 months after surgery. PND was determined using the Z value method. The venous blood sample of the surgical patient was taken before the operation, followed by direct gene sequencing. Statistical methods were used to calculate the correlation between CETP gene polymorphism (rs5882) and PND. RESULTS: The incidence of PND was 29.3% and 18.2% at 7 days and 3 months after operation respectively. The A allele frequency of PND patients was significantly higher than that of non-PND patients 7 days and 3 months after surgery (65.52% vs. 41.43%, 34.48% vs. 58.57%, P=0.001), while the G allele frequency in PND group lower than that of non-PND (58.33% vs. 37.86%, 41.67% vs. 62.14%, P=0.004).AA genotype in PND patients was 34.48%, 38.89% at 7 days and 3 months after surgery respectively, significantly higher than 14.29%, 16.05% of non-PND (P=0.023, P=0.029). CONCLUSION: CETP rs5882 polymorphism is associated with PND and AA genotype may be a predisposing factor for postoperative PND in Chinese Han elderly patient.

Objective:To explore the value of different machine learning models based on Gd-EOB-DTPA enhanced MRI hepatobiliary phase radiomics features in preoperative prediction of microvascular invasion (MVI) of hepatocellular carcinoma (HCC).Methods:The data of 132 patients with HCC confirmed by pathology in the First Affiliated Hospital of Soochow University from January 2015 to May 2020 were retrospectively analyzed, including 72 cases of positive MVI and 60 cases of negative MVI. According to the proportion of 7∶3, the cases were randomly divided into training set and validation set. The radiomics features of hepatobiliary phase images for HCC were extracted by PyRadiomics software. The clinical and radiomics features of the training set were screened by the least absolute shrinkage and selection operator (LASSO) regression with 5 fold cross-validation, and then the optimal feature subset was obtained. Six machine learning algorithms, including decision tree, extreme gradient boosting, random forest, support vector machine (SVM), generalized linear model (GLM) and neural network, were used to build the prediction models, and the ROC curves were used to evaluate the prediction ability of the models. DeLong test was used to compare the differences of area under the curve (AUC) for 6 machine learning algorithms.Results:Totally 14 features selected by LASSO regression were obtained to form the optimal feature subset, including 2 clinical features (maximum tumor diameter and alpha-fetoprotein) and 12 radiomics features. The AUCs of decision tree, extreme gradient boosting, random forest, SVM, GLM and neural network based on the optimal feature subset were 0.969, 1.000, 1.000, 0.991, 0.966, 1.000 in the training set and 0.781, 0.890, 0.920, 0.806, 0.684, 0.703 in the validation set, respectively. There were significant differences in the AUCs between extreme gradient boosting and GLM or neural network ( Z=2.857, 3.220, P=0.004, 0.001). The differences in AUCs between random forest and SVM, GLM, or neural network were significant ( Z=2.371, 3.190, 3.967, P=0.018, 0.001,<0.001). The difference in AUCs between SVM and GLM was statistically significant ( Z=2.621 , P=0.009). There were no significant differences in the AUCs among the other machine learning models ( P>0.05). Conclusion:Machine learning models based on Gd-EOB-DTPA enhanced MRI hepatobiliary phase radiomics features can be used to preoperatively predict MVI of HCC, particularly the extreme gradient boosting and random forest models have high prediction efficiency.

Objective:To explore the value of spectral CT radiomics quantitative features on differentiating lung cancer nodule from inflammatory nodule.Methods:The spectral CT imaging data of 96 lung cancer nodules and 45 inflammatory nodules from the First Affiliated Hospital of Soochow University were analyzed retrospectively. According to a ratio of two to one, patients were randomly assigned to the training group and validation group, including 64 lung cancer nodules and 30 inflammatory nodules in the training group, 32 lung cancer nodules and 15 inflammatory nodules in the validation group. MaZda software was used for radiomic feature extraction from the 70 keV monochromatic images in arterial phase and venous phase for lung cancer nodules and inflammatory nodules in the training group. Fisher coefficients (Fisher), classification error probability combined average correlation coefficients (POE+ACC) and mutual information (MI) were used to select 10 optimal features for the optimal feature subsets. The optimal feature subsets were analyzed by using linear discriminant analysis (LDA) and nonlinear discriminant analysis (NDA) to calculate the area under the receiver operating characteristic (ROC) curve (AUC), accuracy, sensitivity, specificity, precise and F1 score in differentiating lung cancer nodule from inflammatory nodule. The prediction model was established using the optimal feature subsets in the training group with artificial neural network (ANN). Then the established prediction model was used to differentiate lung cancer nodule from inflammatory nodule in the validation group. Delong test was used to compare the differences in the AUC of different optimal feature subsets.Results:In arterial phase, the optimal feature subset obtained from MI-NDA had the highest AUC of 0.888 [95% confidence interval (CI) 0.806-0.943], accuracy rate of 88.3%, sensitivity of 87.5% and specificity of 90.0%, on the differential diagnosis of lung cancer nodule and inflammatory nodule in the training group. There was no significant difference in AUC between MI-NDA and Fisher-NDA or (POE+ACC)-NDA method ( Z=1.941, P=0.052; Z=1.683, P=0.092). In venous phase, the optimal feature subset obtained from (POE+ACC)-NDA had the highest AUC of 0.846 (95%CI 0.757-0.912), accuracy rate of 87.2%, sensitivity of 92.2% and specificity of 76.7%, on the differential diagnosis of lung cancer nodule and inflammatory nodule in the training group. There was no significant difference in AUC between(POE+ACC)-NDA and MI-NDA method ( Z=1.354, P=0.18), but significant difference between (POE+ACC)-NDA and Fisher-NDA method ( Z=2.423, P=0.015). In the validation group and training group, the optimal feature subset selected by MI-NDA method had the highest AUC of 0.888(95%CI 0.806-0.943) and 0.871(95%CI 0.741-0.951). Conclusion:Spectral CT radiomics quantitative features have great value on the differential diagnosis of lung cancer nodule and inflammatory nodule.

Objective:To explore the value of gadolinium-ethoxybenzyl- diethylenetriamine pentaacetic acid (Gd-EOB-DTPA) enhanced MRI nomogram model for preoperative prediction of Ki-67 expression in hepatocellular carcinoma (HCC).Methods:Data of 85 patients of HCC confirmed by postoperative pathology, who underwent preoperative Gd-EOB-DTPA enhanced MRI between September 2016 and August 2019 in the First Affiliated Hospital of Soochow University were retrospectively evaluated. According to postoperative immunohistochemistry Ki-67 index, the 85 patients were divided into Ki-67 low expression group(Ki-67 index ≤10%, n=20) and Ki-67 high expression group (Ki-67 index >10%, n=65). Clinical data (hepatitis, cirrhosis, etc.), qualitative imaging parameters (tumor margin, capsule, etc.) were compared by χ 2 test and quantitative parameters [lesion-to-normal parenchyma ratio-arterial phase (LNR-AP), lesion-to-normal parenchyma ratio-portal phase (LNR-PP), lesion-to-normal parenchyma ratio-equilibrium phase (LNR-EP) and lesion-to-normal parenchyma ratio-hepatobiliary phase (LNR-HBP)] were compared by independent sample t test. The above statistically significant parameters were included in multivariate logistic regression to identify the independent predictors of Ki-67 high expression and then the nomogram model for predicting Ki-67 expression of HCC was established. Results:alpha-fetoprotein (AFP) tumor margin, arterial rim enhancement between the Ki-67 low expression group and the Ki-67 high expression group had significant differences (χ 2 were 8.196, 10.538 and 4.717, respectively, P<0.05). LNR-AP, LNR-PP, LNR-EP and LNR-HBP between the two groups had significant differences ( t were 2.929, 2.773, 2.890 and 3.437, respectively, P<0.05).The result of multivariate logistic regression revealed that AFP≥20 μg/L, non-smooth tumor margin and low LNR-HBP were the independent predictors of Ki-67 high expression (odds ratio were 4.090, 3.509 and 0.042, respectively, P<0.05).The Gd-EOB-DTPA enhanced MRI nomogram model for predicting Ki-67 expression of HCC was established successfully. The Area under the receiver operating characteristic curve of the nomogram was 0.837 and the corrected predictive curve fitted the ideal curve, which suggested the model had a good predictive efficiency. Conclusion:Gd-EOB-DTPA enhanced MRI nomogram model has great value in preoperative prediction of Ki-67 expression of HCC, which provided a personalized prediction method for Ki-67 expression in patient with HCC.

Objective:To investigate the hippocampal subfield volumes before and after taking hydrochloric donepezil in patients with mild Alzheimer's disease (AD). Methods:From January, 2017 to June, 2018, 25 mild AD patients accepted hydrochloric donepezil (treatment group), 25 patients accepted placebo (placebo group) and 25 age- and gender-matched healthy old people (control group) were scanned with magnetic resonance imaging (MRI) under 3D-T₁ TFE sequence before and after a six-month treatment, and the automated segmentation of the hippocampus subfields was fulfilled with FreeSurfer and the hippocampus subfield volumes were compared, while the patients were assessed with Mini-Mental State Examination (MMSE). Results:Compared with the control group, the volumes of left CA1， CA2-3 and CA4-DG, and right CA1 and CA2-3 were reduced in patients before treatment (t > 2.294, P < 0.05). The volume of left CA4-DG increased in the treatment group compared with that of the placebo group after treatment (t = 2.196, P < 0.05), and the volume of bilateral CA1 and CA2-3 tended to increase but not significantly (t < 1.888, P > 0.065). The MMSE score was more in the treatment group than in the placebo group after treatment (t = 2.764, P < 0.05). Conclusion:The association with asymmetric atrophy in the hippocampal subfields has been found in mild AD patients, especially in left CA4-DG, which may be used as a valuable marker for diagnosis and evaluation for treatment at early stage.

Objectives: To establish a geographic information application system for analyzing the spatial and temporal distribution of major infectious diseases in various regions of the world and to assess the risk of importation of those diseases, to China. Methods: We collected and integrated the following information on: 1) outbreaks and areas of epidemics of major infectious diseases in the world from 2000 to 2017, 2) cases of infectious diseases in arriving travelers through active surveillance at international entry-exit ports in mainland China from 2014 to 2016, 3) numbers of annual global international flights and travelers in the country. With the above information, a global space-time distribution database on major infectious diseases was then established, using the technology related to the system. Models regarding technologies on time-space analysis, probabilistic risk assessment and geographic information visualization, were applied to establish a geographic information system on risk assessment of infectious diseases that imported to China. Results: Through integration of information on outbreaks and epidemic areas of 60 major infectious diseases in 220 countries and regions around the world, as well as 42 kinds of infectious diseases identified among the international arrivals in mainland China, a system was then developed. Information on the distribution of major infectious diseases and their potential risks in the worldwide various regions, characteristics of spectrum and disease burden of infectious diseases imported to each province of mainland China were displayed. Thus, risks on importing infectious diseases in each province via air way were able to be evaluated and simulated by the probabilistic risk assessment model, under the information on specific kind of infectious disease, outside China. Conclusion Geographic Information System on Risk Assessment Regarding Infectious Diseases Imported to China provides basic data for epidemiological reconnaissance and assessment on risks of importing infectious diseases outside China, thus would be helpful for the improvement of strategies on surveillance, prevention and control regarding the importing infectious diseases, in China.

OBJECTIVE: To identify the optimal factors in diffusion tensor imaging for predicting corticospinal tract (CST) injury caused by brain tumors. MATERIALS AND METHODS: This prospective study included 33 patients with motor weakness and 64 patients with normal motor function. The movement of the CST, minimum distance between the CST and the tumor, and relative fractional anisotropy (rFA) of the CST on diffusion tensor imaging, were compared between patients with motor weakness and normal function. Logistic regression analysis was used to obtain the optimal factor predicting motor weakness. RESULTS: In patients with motor weakness, the displacement (8.44 ± 6.64 mm) of the CST (p = 0.009), minimum distance (3.98 ± 7.49 mm) between the CST and tumor (p < 0.001), and rFA (0.83 ± 0.11) of the CST (p < 0.001) were significantly different from those of the normal group (4.64 ± 6.65 mm, 14.87 ± 12.04 mm, and 0.98 ± 0.05, respectively) (p = 0.009, p < 0.001, and p < 0.001). The frequencies of patients with the CST passing through the tumor (6%, p = 0.002), CST close to the tumor (23%, p < 0.001), CST close to a malignant tumor (high grade glioma, metastasis, or lymphoma) (19%, p < 0.001), and CST passing through infiltrating edema (19%, p < 0.001) in the motor weakness group, were significantly different from those of the patients with normal motor function (0, 8, 1, and 10%, respectively). Logistic regression analysis showed that decreased rFA and CST close to a malignant tumor were effective variables related to motor weakness. CONCLUSION: Decreased fractional anisotropy, combined with closeness of a malignant tumor to the CST, is the optimal factor in predicting CST injury caused by a brain tumor.
Anisotropy ; Brain Neoplasms* ; Brain* ; Diffusion Tensor Imaging* ; Diffusion* ; Edema ; Glioma ; Humans ; Logistic Models ; Magnetic Resonance Imaging ; Neoplasm Metastasis ; Prospective Studies ; Pyramidal Tracts*